9 citations · 14 across the 12 of their papers we have counts for
12 papers
Soundness-Aware Level: A Microscopic Signature that Predicts LLM Reasoning Potential
Xuansheng Wu, Xiaoman Pan, Wenlin Yao +1
Reinforcement learning with verifiable rewards (RLVR) can elicit strong reasoning in large language models (LLMs), while their performance after RLVR varies dramatically across dif…
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Guangchen Lan, Sipeng Zhang, Tianle Wang +7
As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving perfor…
Bridging SFT and DPO for Diffusion Model Alignment with Self-Sampling Preference Optimization
Daoan Zhang, Guangchen Lan, Dong-Jun Han +8
Existing post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement learning (RL) methods; the former is stable during training but suffer…
DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes
Zhaowei Wang, Hongming Zhang, Tianqing Fang +6
Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. However, their potential to comprehend e…
Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots
Hongming Zhang, Xiaoman Pan, Hongwei Wang +3
We introduce Cognitive Kernel, an open-source agent system towards the goal of generalist autopilots. Unlike copilot systems, which primarily rely on users to provide essential sta…
Abstraction-of-Thought Makes Language Models Better Reasoners
Ruixin Hong, Hongming Zhang, Xiaoman Pan +2
Abstract reasoning, the ability to reason from the abstract essence of a problem, serves as a key to generalization in human reasoning. However, eliciting language models to perfor…
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models
Xinran Zhao, Hongming Zhang, Xiaoman Pan +4
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by…
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
Rui Yang, Xiaoman Pan, Feng Luo +4
We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful and harmless AI systems. However, it is g…
Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention
Kaiqiang Song, Xiaoyang Wang, Sangwoo Cho +2
This paper introduces a novel approach to enhance the capabilities of Large Language Models (LLMs) in processing and understanding extensive text sequences, a critical aspect in ap…
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Wenhao Yu, Hongming Zhang, Xiaoman Pan +3
Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveragin…
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
Xuansheng Wu, Wenlin Yao, Jianshu Chen +4
Large Language Models (LLMs) have achieved remarkable success, where instruction tuning is the critical step in aligning LLMs with user intentions. In this work, we investigate how…
LASER: LLM Agent with State-Space Exploration for Web Navigation
Kaixin Ma, Hongming Zhang, Hongwei Wang +3
Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. While achieving decent performance, previous methods implicit…